In clinical examinations of blood vessels using X-ray technique (angiography) a contrast media is injected into the selected blood vessel to increase the contrast and the resolution of the blood vessels relative to the surrounding tissue. It is well known that a further increase of the image quality is achieved by un-mashing the contributions in the resulting image that is unaffected by the the contrast media e.g. bones. This method is commonly referred to as `Digital Subtraction Angiography (DSA)` and require a series (or sequence) of at least two images recorded during the contrast injection. In the first image no contrast media is present (pre contrast) while in the following images the blood vessels which are injected by contrast media will appear with increased visibility while bone and tissue not being affected by the contrast media will give a constant contribution throughout the sequence. If two consecutive images from such an angiography sequence are subtracted, this operation will in an ideal case remove the static structures while rendering the blood vessels which are subject to contrast media without masking effects from surrounding body parts. Digital subtraction angiography is a well known and frequently used method which exists in a number of varieties. One of the most frequently used algorithms computes the absolute value of the difference between every consecutive image pair in the sequence. The result is then computed by seeking the maximal value for each individual image element (pixel) in the subtracted sequence. Successful use of subtraction angiography is dependent on the fact that all changes or rather motions, that occur in the image sequence are induced by the injection of the contrast medium. All external motions not induced by the contrast medium injection generate what is termed `motion artifacts` when using traditional subtraction angiography algorithms. Disturbing artifacts will occur in the following cases
1. Noise Noise is present in the X-ray equipment and in the recording process where the registered intensity is digitized (quanfization noise). PA1 2. Pure translation The patient fails to be immobile and translates the part of the body which is subject for the investigation during the recording. PA1 3. Body motion The patient fails to be immobile and rotation is involved in the motion of the part of the body which is subject for the investigation during the recording. PA1 4. Internal motion Motion of inner organs such as e.g. intestinal motions, movements of the lungs and the chest during breathing and the movements generated by the beating heart.
A rather small motion artifact is able to cause a substantial degradation in image quality. To avoid the costs as well as the risks and the suffering involved in a new recording it is of major importance that the effects of motion artifacts are reduced in the post processing of the angiography sequence. In case 2 above it is quite simple to restore the recording since the motion is uniform for all pixels belonging to the same image in the sequence. The sequence can consequently be restored by performing an equally large shift in the `opposite direction` in the post processing for the images that where affected by the motion. This method is termed `pixel shift`. Pixel shift is widely used and works satisfactory for such simple motion artifacts. The magnitude of the shift, however, needs to be defined by an human operator. The implementation of the pixel shift with sub-pixel accuracy is straight forward using fundamental signal processing methods [Bracewell, chapter 10].
Since the description of the new invention will be made in terms of spatio-temporal filters it may at this point be illuminating to express the above standard methods in filter terms to clarify the differences. The fundamental operation using DSA is to subtract two images. The recorded image sequence can be interpreted as a three dimensional space time signal with two spatial and one temporal dimension. In filter terminology a spatio-temporal filter performing subtraction consists of only two coefficients, plus and minus one, positioned on the temporal axis. The pixel shift operation, on the other hand, is performed separately for each single image in the sequence and the corresponding spatio-temporal filter has no temporal extent. To reduce artifacts induced by noise (case one above) it is known that a temporal filter tuned to the spectral content of the contrast injection envelope can be used, (U.S. Pat. No. 5,504,980). By this approach only the fractional part of the noise that `varies` in the same pace as the contrast pulse will contribute to artifacts. A conventional DSA algorithm is applied after this preprocessing step. Common for the above operations is that the shape of these filters are global, i.e. constant for a whole image or for the entire sequence. Using established methods there is consequently no possibility to adapt the filters to motions that only occur locally in a small environment of the spatio-temporal signal e.g. the type of motions that are described in point three and four above. Consider for example a recording of a heart where the coronary arteries are subject to the motions of the heart beats as well as the motions induced by the contrast medium.
The velocity and direction of the movements of the heart vary locally over the image and over time. There is no possibility that the above described methods could be expected to restore such complex angiography sequences with satisfactory result. The support for this invention rests on the advanced image processing tools which have been developed for local image sequence structure analysis